-
Book Overview & Buying
-
Table Of Contents
Forecasting Time Series Data with Facebook Prophet
By :
Forecasting Time Series Data with Facebook Prophet
By:
Overview of this book
Prophet enables Python and R developers to build scalable time series forecasts. This book will help you to implement Prophet’s cutting-edge forecasting techniques to model future data with higher accuracy and with very few lines of code. You will begin by exploring the evolution of time series forecasting, from the basic early models to the advanced models of the present day. The book will demonstrate how to install and set up Prophet on your machine and build your first model with only a few lines of code. You'll then cover advanced features such as visualizing your forecasts, adding holidays, seasonality, and trend changepoints, handling outliers, and more, along with understanding why and how to modify each of the default parameters. Later chapters will show you how to optimize more complicated models with hyperparameter tuning and by adding additional regressors to the model. Finally, you'll learn how to run diagnostics to evaluate the performance of your models and see some useful features when running Prophet in production environments.
By the end of this Prophet book, you will be able to take a raw time series dataset and build advanced and accurate forecast models with concise, understandable, and repeatable code.
Table of Contents (18 chapters)
Preface
Section 1: Getting Started
Chapter 1: The History and Development of Time Series Forecasting
Chapter 2: Getting Started with Facebook Prophet
Section 2: Seasonality, Tuning, and Advanced Features
Chapter 3: Non-Daily Data
Chapter 4: Seasonality
Chapter 5: Holidays
Chapter 6: Growth Modes
Chapter 7: Trend Changepoints
Chapter 8: Additional Regressors
Chapter 9: Outliers and Special Events
Chapter 10: Uncertainty Intervals
Section 3: Diagnostics and Evaluation
Chapter 11: Cross-Validation
Chapter 12: Performance Metrics
Chapter 13: Productionalizing Prophet